图 书 馆
数据抽样策略如何影响社会媒体中信息扩散的发现？How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? |
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| 课程网址: | http://videolectures.net/icwsm2010_dechoudhury_hdd/ |
| 主讲教师: | Munmun De Choudhury |
| 开课单位: | 佐治亚理工学院 |
| 开课时间: | 2010-06-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant computational challenges due to the enormous rate of production of new information: researchers are therefore, often forced to analyze a judiciously selected “sample” of the data. Like other social media phenomena, information diffusion is a social process–it is affected by user context, and topic, in addition to the graph topology. This paper studies the impact of different attribute and topology based sampling strategies on the discovery of an important social media phenomena–information diffusion. We examine several widely-adopted sampling methods that select nodes based on attribute (random, location, and activity) and topology (forest fire) as well as study the impact of attribute based seed selection on topology based sampling. Then we develop a series of metrics for evaluating the quality of the sample, based on user activity (e.g. volume, number of seeds), topological (e.g. reach, spread) and temporal characteristics (e.g. rate). We additionally correlate the diffusion volume metric with two external variables–search and news trends. Our experiments reveal that for small sample sizes (30%), a sample that incorporates both topology and user context (e.g. location, activity) can improve on naive methods by a significant margin of ~15-20%. |
| 关 键 词: | 社交媒体; 社交过程; 拓扑 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-26：lxf |
| 阅读次数: | 23 |